通过近距离政策优化算法在低附着路况下对车辆横向稳定性控制的研究.
Honglei Pang1, He Huang2, Yangping Fan2,3
1School of Transportation Engineering, Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu, Peoples R China.
PloS one
|November 26, 2025
概括
本研究介绍了一种使用近距离政策优化 (PPO) 的智能稳定性控制算法,以实现更安全的紧急制动. PPO算法在滑动和不平坦的道路上增强了车辆的控制,在危险的驾驶条件下提高了安全.
科学领域:
- 智能运输系统 智能运输系统
- 控制工程 控制工程 控制工程
- 机器学习 机器学习
背景情况:
- 车辆的横向稳定性对于主动安全至关重要,特别是在低粘附面或不均的粘附面上紧急制动时.
- 现有的控制方法在这种危险条件下难以保持稳定性.
研究的目的:
- 为紧急制动提出一个智能集成的纵向和横向稳定控制算法.
- 通过近距离政策优化 (PPO) 算法在具有挑战性的路面上增强车辆的稳定性.
主要方法:
- 在Amesim开发了高保真电机制动 (EMB) 和Steer-by-Wire (SBW) 系统模型,并在CarSim中开发了全车动力学模型.
- 设计了一个连续状态和动作空间,用于强化学习,包括车辆状态和道路参数.
- 利用Amesim-CarSim-Python共同模拟平台在各种紧急制动场景下训练PPO算法.
主要成果:
- 与模型预测控制 (MPC) 和滑动模式控制 (SMC) 相比,PPO算法显示出更高的性能.
- 在低附着性道路上降低了15-20%的制动距离,在分裂微米道路上降低了25-30%的横向偏差.
- 在曲的道路上减低了28.8%的曲率振荡,硬件在循环 (HIL) 验证证实了强度.
结论:
- 拟议的基于PPO的智能控制算法在困难的路面上紧急制动时有效地保持车辆的侧向稳定性.
- 该算法在制动距离,横向偏差和曲率控制方面提供了显著的改进,提高了整体车辆安全.
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